Search results for: maximal data sets

Authors: Yasuyuki Takahashi, Maya Yamashita, Kyoko Saito

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In general, dynamic SPECT data acquisition needs a few minutes for one rotation. Thus, the time-activity curve (TAC) derived from the dynamic SPECT is relatively coarse. In order to effectively shorten the interval, between data points, we adopted a 180-degree interpolation method. This method is already used for reconstruction of the X-ray CT data. In this study, we applied this 180-degree interpolation method to SPECT and investigated its effectiveness.To briefly describe the 180-degree interpolation method: the 180-degree data in the second half of one rotation are combined with the 180-degree data in the first half of the next rotation to generate a 360-degree data set appropriate for the time halfway between the first and second rotations. In both a phantom and a patient study, the data points from the interpolated images fell in good agreement with the data points tracking the accumulation of 99mTc activity over time for appropriate region of interest. We conclude that data derived from interpolated images improves the apparent time resolution of dynamic SPECT.

Keywords: dynamic SPECT, time resolution, 180-degree interpolation method, 99mTc-GSA.

Procedia PDF Downloads 485

Authors: Sandeep Thakur, Ismail Mondal, Phani Bhusan Ghosh, Papita Das, Tarun Kumar De

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Sundarbans Reserve forest of India has been undergoing major transformations in the recent past owing to population pressure and related changes. This has brought about major changes in the spatial landscape of the region especially in the western parts. This study attempts to assess the impacts of the Landcover changes on the mangrove habitats. Time series imageries of Landsat were used to analyze the Normalized Differential Vegetation Index (NDVI) patterns over the western parts of Indian Sundarbans forest in order to assess the heath of the mangroves in the region. The images were subjected to Land use Land cover (LULC) classification using sub-pixel classification techniques in ERDAS Imagine software and the changes were mapped. The spatial proliferation of aquaculture farms during the study period was also mapped. A multivariate regression analysis was carried out between the obtained NDVI values and the LULC classes. Similarly, the observed meteorological data sets (time series rainfall and minimum and maximum temperature) were also statistically correlated for regression. The study demonstrated the application of NDVI in assessing the environmental status of mangroves as the relationship between the changes in the environmental variables and the remote sensing based indices felicitate an efficient evaluation of environmental variables, which can be used in the coastal zone monitoring and development processes.

Keywords: aquaculture farms, LULC, Mangrove, NDVI

Procedia PDF Downloads 163

Authors: Mohammadreza Akbari, Pooya Soleimani Besheli, Reza Khalili, Davood Domiri Danji

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In this conference, our aims are accuracy, capabilities and power at solving of the complicated non-linear partial differential. Our purpose is to enhance the ability to solve the mentioned nonlinear differential equations at basic science and engineering field and similar issues with a simple and innovative approach. As we know most of engineering system behavior in practical are nonlinear process (especially basic science and engineering field, etc.) and analytical solving (no numeric) these problems are difficult, complex, and sometimes impossible like (Fluids and Gas wave, these problems can't solve with numeric method, because of no have boundary condition) accordingly in this symposium we are going to exposure an innovative approach which we have named it Akbari-Ganji's Method or AGM in engineering, that can solve sets of coupled nonlinear differential equations (ODE, PDE) with high accuracy and simple solution and so this issue will emerge after comparing the achieved solutions by Numerical method (Runge-Kutta 4th). Eventually, AGM method will be proved that could be created huge evolution for researchers, professors and students in whole over the world, because of AGM coding system, so by using this software we can analytically solve all complicated linear and nonlinear partial differential equations, with help of that there is no difficulty for solving all nonlinear differential equations. Advantages and ability of this method (AGM) as follow: (a) Non-linear Differential equations (ODE, PDE) are directly solvable by this method. (b) In this method (AGM), most of the time, without any dimensionless procedure, we can solve equation(s) by any boundary or initial condition number. (c) AGM method always is convergent in boundary or initial condition. (d) Parameters of exponential, Trigonometric and Logarithmic of the existent in the non-linear differential equation with AGM method no needs Taylor expand which are caused high solve precision. (e) AGM method is very flexible in the coding system, and can solve easily varieties of the non-linear differential equation at high acceptable accuracy. (f) One of the important advantages of this method is analytical solving with high accuracy such as partial differential equation in vibration in solids, waves in water and gas, with minimum initial and boundary condition capable to solve problem. (g) It is very important to present a general and simple approach for solving most problems of the differential equations with high non-linearity in engineering sciences especially at civil engineering, and compare output with numerical method (Runge-Kutta 4th) and Exact solutions.

Keywords: new approach, AGM, sets of coupled nonlinear differential equation, exact solutions, numerical

Procedia PDF Downloads 446

Authors: N. D'Amico, E. Grossi, B. Colombo, F. Rigiroli, M. Buscema, D. Fazzini, G. Cornalba, S. Papa

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Purpose: To characterize, through a radiomic approach, the nature of nodules considered non-specific by expert radiologists, recognized in magnetic resonance mammography (MRm) with T1-weighted (T1w) sequences with paramagnetic contrast. Material and Methods: 47 cases out of 1200 undergoing MRm, in which the MRm assessment gave uncertain classification (non-specific nodules), were admitted to the study. The clinical outcome of the non-specific nodules was later found through follow-up or further exams (biopsy), finding 35 benign and 12 malignant. All MR Images were acquired at 1.5T, a first basal T1w sequence and then four T1w acquisitions after the paramagnetic contrast injection. After a manual segmentation of the lesions, done by a radiologist, and the extraction of 150 radiomic features (30 features per 5 subsequent times) a machine learning (ML) approach was used. An evolutionary algorithm (TWIST system based on KNN algorithm) was used to subdivide the dataset into training and validation test and to select features yielding the maximal amount of information. After this pre-processing, different machine learning systems were applied to develop a predictive model based on a training-testing crossover procedure. 10 cases with a benign nodule (follow-up older than 5 years) and 18 with an evident malignant tumor (clear malignant histological exam) were added to the dataset in order to allow the ML system to better learn from data. Results: NaiveBayes algorithm working on 79 features selected by a TWIST system, resulted to be the best performing ML system with a sensitivity of 96% and a specificity of 78% and a global accuracy of 87% (average values of two training-testing procedures ab-ba). The results showed that in the subset of 47 non-specific nodules, the algorithm predicted the outcome of 45 nodules which an expert radiologist could not identify. Conclusion: In this pilot study we identified a radiomic approach allowing ML systems to perform well in the diagnosis of a non-specific nodule at MR mammography. This algorithm could be a great support for the early diagnosis of malignant breast tumor, in the event the radiologist is not able to identify the kind of lesion and reduces the necessity for long follow-up. Clinical Relevance: This machine learning algorithm could be essential to support the radiologist in early diagnosis of non-specific nodules, in order to avoid strenuous follow-up and painful biopsy for the patient.

Keywords: breast, machine learning, MRI, radiomics

Procedia PDF Downloads 256

Authors: Inigo de Miguel Beriain

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Genetic data of deceased persons are of great interest for both biomedical research and clinical use. This is due to several reasons. On the one hand, many of our diseases have a genetic component; on the other hand, we share genes with a good part of our biological family. Therefore, it would be possible to improve our response considerably to these pathologies if we could use these data. Unfortunately, at the present moment, the status of data on the deceased is far from being satisfactorily resolved by the EU data protection regulation. Indeed, the General Data Protection Regulation has explicitly excluded these data from the category of personal data. This decision has given rise to a fragmented legal framework on this issue. Consequently, each EU member state offers very different solutions. For instance, Denmark considers the data as personal data of the deceased person for a set period of time while some others, such as Spain, do not consider this data as such, but have introduced some specifically focused regulations on this type of data and their access by relatives. This is an extremely dysfunctional scenario from multiple angles, not least of which is scientific cooperation at the EU level. This contribution attempts to outline a solution to this dilemma through an alternative proposal. Its main hypothesis is that, in reality, health data are, in a sense, a rara avis within data in general because they do not refer to one person but to several. Hence, it is possible to think that all of them can be considered data subjects (although not all of them can exercise the corresponding rights in the same way). When the person from whom the data were obtained dies, the data remain as personal data of his or her biological relatives. Hence, the general regime provided for in the GDPR may apply to them. As these are personal data, we could go back to thinking in terms of a general prohibition of data processing, with the exceptions provided for in Article 9.2 and on the legal bases included in Article 6. This may be complicated in practice, given that, since we are dealing with data that refer to several data subjects, it may be complex to refer to some of these bases, such as consent. Furthermore, there are theoretical arguments that may oppose this hypothesis. In this contribution, it is shown, however, that none of these objections is of sufficient substance to delegitimize the argument exposed. Therefore, the conclusion of this contribution is that we can indeed build a general framework on the processing of personal data of deceased persons in the context of the GDPR. This would constitute a considerable improvement over the current regulatory framework, although it is true that some clarifications will be necessary for its practical application.

Keywords: collective data conceptual issues, data from deceased people, genetic data protection issues, GDPR and deceased people

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Authors: L. Grinis, N. Lubashevsky, Y. Ostrovski

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A jet pump is a type of pump that accelerates the flow of a secondary fluid (driven fluid) by introducing a motive fluid with high velocity into a converging-diverging nozzle. Jet pumps are also known as adductors or ejectors depending on the motivator phase. The ejector's motivator is of a gaseous nature, usually steam or air, while the educator's motivator is a liquid, usually water. Jet pumps are devices that use air bubbles and are widely used in wastewater treatment processes. In this work, we will discuss about the characteristics of the jet pump and the computational simulation of this device. To find the optimal angle and depth for the air pipe, so as to achieve the maximal air volumetric flow rate, an experimental apparatus was constructed to ascertain the best geometrical configuration for this new type of jet pump. By using 3D printing technology, a series of jet pumps was printed and tested whilst aspiring to maximize air flow rate dependent on angle and depth of the air pipe insertion. The experimental results show a major difference of up to 300% in performance between the different pumps (ratio of air flow rate to supplied power) where the optimal geometric model has an insertion angle of 60⁰ and air pipe insertion depth ending at the center of the mixing chamber. The differences between the pumps were further explained by using CFD for better understanding the reasons that affect the airflow rate. The validity of the computational simulation and the corresponding assumptions have been proved experimentally. The present research showed high degree of congruence with the results of the laboratory tests. This study demonstrates the potential of using of the jet pump in many practical applications.

Keywords: air bubbles, CFD simulation, jet pump, applications

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Authors: Dainius Genys, Vylius Leonavicius, Ricardas Krikstolaitis

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Energy security problems in Lithuania are analyzed on a regular basis; however, there is no comprehensive research on the very issue of the concept of public energy security. There is a lack of attention not only to social determinants of perception of energy security, but also a lack of a deeper analysis of the public opinion. This article aims to research the Lithuanian public perception of energy security. Complex tasks were set during the sociological study. Survey questionnaire consisted of different sets of questions: view of energy security (risk perception, political orientation, and energy security; comprehensiveness and energy security); view of energy risks and threats (perception of energy safety factors; individual dependence and burden; disobedience and risk); view of the activity of responsible institutions (energy policy assessment; confidence in institutions and energy security), demographic issues. In this article, we will focus on two aspects: a) We will analyze public opinion on the most important aspects of energy security and social factors influencing them; The hypothesis is made that public perception of energy security is related to value orientations: b) We will analyze how public opinion on energy policy executed by the government and confidence in the government are intertwined with the concept of energy security. Data of the survey, conducted on May 10-19 and June 7-17, 2013, when Seimas and the government consisted of the coalition dominated by Social Democrats with Labor, Order and Justice Parties and the Electoral Action of Poles, were used in this article. It is important to note that the survey was conducted prior to Russia’s occupation of the Crimea.

Keywords: energy security, public opinion, risk, energy threat, energy security policy

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Authors: Abdullah I. Alkraiji, Thomas W. Jackson, Ian Murray

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The proliferation of health data standards today is somewhat overlapping and conflicting, resulting in market confusion and leading to increasing proprietary interests. The government role and support in standardization for health data are thought to be crucial in order to establish credible standards for the next decade, to maximize interoperability across the health sector, and to decrease the risks associated with the implementation of non-standard systems. The normative literature missed out the exploration of the different steps required to be undertaken by the government towards the development of national health data standards. Based on the lessons learned from a qualitative study investigating the different issues to the adoption of health data standards in the major tertiary hospitals in Saudi Arabia and the opinions and feedback from different experts in the areas of data exchange and standards and medical informatics in Saudi Arabia and UK, a list of steps required towards the development of national health data standards was constructed. Main steps are the existence of: a national formal reference for health data standards, an agreed national strategic direction for medical data exchange, a national medical information management plan and a national accreditation body, and more important is the change management at the national and organizational level. The outcome of this study can be used by academics and practitioners to develop the planning of health data standards, and in particular those in developing countries.

Keywords: interoperabilty, medical data exchange, health data standards, case study, Saudi Arabia

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Authors: SangWon Han, MuWook Pyeon, Sujung Moon, DaeKyo Seo

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Recently, technologies based on three-dimensional (3D) space information are being developed and quality of life is improving as a result. Research on real-time digital map (RDM) is being conducted now to provide 3D space information. RDM is a service that creates and supplies 3D space information in real time based on location/shape detection. Research subjects on RDM include the construction of 3D space information with matching image data, complementing the weaknesses of image acquisition using multi-source data, and data collection methods using big data. Using RDM will be effective for space analysis using 3D space information in a U-City and for other space information utilization technologies.

Keywords: RDM, multi-source data, big data, U-City

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: Nieto Bernal Wilson, Carmona Suarez Edgar

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The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse

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Authors: Go Bong Choi, Shin Je Lee, Sung Jin Yoo, Gibaek Lee, Jong Min Lee

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In South Korea, it is difficult to obtain data for statistical pipe assessment. In this paper, to address these issues, we find that various statistical model presented before is how data mixed with noise and are whether apply in South Korea. Three major type of model is studied and if data is presented in the paper, we add noise to data, which affects how model response changes. Moreover, we generate data from model in paper and analyse effect of noise. From this we can find robustness and applicability in Korea of each model.

Keywords: proportional hazard model, survival model, water main deterioration, ecological sciences

Procedia PDF Downloads 728

Authors: Sivakumar Kottapalli, Abdesselam Abdelouas, Christoph Hartnack

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Spent nuclear fuel generates a mixed field of ionizing radiation to the water. This radiation field is generally dominated by gamma rays and a limited flux of fast neutrons. The fuel cladding effectively attenuates beta and alpha particle radiation. Small fraction of the spent nuclear fuel exhibits some degree of fuel cladding penetration due to pitting corrosion and mechanical failure. Breaches in the fuel cladding allow the exposure of small volumes of water in the cask to alpha and beta ionizing radiation. The safety of the transport of radioactive material is assured by the package complying with the IAEA Requirements for the Safe Transport of Radioactive Material SSR-6. It is of high interest to avoid generation of hydrogen inside the cavity which may to an explosive mixture. The risk of hydrogen production along with other radiation gases should be analyzed for a typical spent fuel for safety issues. This work aims to perform a realistic study of the production of hydrogen by radiolysis assuming most penalizing initial conditions. It consists in the calculation of the radionuclide inventory of a pellet taking into account the burn up and decays. Westinghouse 17X17 PWR fuel has been chosen and data has been analyzed for different sets of enrichment, burnup, cycles of irradiation and storage conditions. The inventory is calculated as the entry point for the simulation studies of hydrogen production by radiolysis kinetic models by MAKSIMA-CHEMIST. Dose rates decrease strongly within ~45 μm from the fuel surface towards the solution(water) in case of alpha radiation, while the dose rate decrease is lower in case of beta and even slower in case of gamma radiation. Calculations are carried out to obtain spectra as a function of time. Radiation dose rate profiles are taken as the input data for the iterative calculations. Hydrogen yield has been found to be around 0.02 mol/L. Calculations have been performed for a realistic scenario considering a capsule containing the spent fuel rod. Thus, hydrogen yield has been debated. Experiments are under progress to validate the hydrogen production rate using cyclotron at > 5MeV (at ARRONAX, Nantes).

Keywords: radiolysis, spent fuel, hydrogen, cyclotron

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Authors: Jefriza, Habibah Lateh, Saumi Syahreza

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The 2016 Mw 6.5 Pidie Jaya earthquake is one of the biggest disasters that has occurred in Aceh within the last five years. This earthquake has caused severe damage to many infrastructures such as schools, hospitals, mosques, and houses in the district of Pidie Jaya and surrounding areas. Earthquakes commonly occur in Aceh Province due to the Aceh-Sumatra is located in the convergent boundaries of the Sunda Plate subducted beneath the Indo-Australian Plate. This convergence is responsible for the intensification of seismicity in this region. The plates are tilted at a speed of 63 mm per year and the right lateral component is accommodated by strike- slip faulting within Sumatra, mainly along the great Sumatran fault. This paper presents preliminary findings of InSAR study aimed at investigating the co-seismic surface deformation pattern in Pidie Jaya, Aceh-Indonesia. Coseismic surface deformation is rapid displacement that occurs at the time of an earthquake. Coseismic displacement mapping is required to study the behavior of seismic faults. InSAR is a powerful tool for measuring Earth surface deformation to a precision of a few centimetres. In this study, two radar images of the same area but at two different times are required to detect changes in the Earth’s surface. The ascending and descending Sentinel-1A (S1A) synthetic aperture radar (SAR) data and Sentinels application platform (SNAP) toolbox were used to generate SAR interferogram image. In order to visualize the InSAR interferometric, the S1A from both master (26 Nov 2016) and slave data-sets (26 Dec 2016) were utilized as the main data source for mapping the coseismic surface deformation. The results show that the fringes of phase difference have appeared in the border region as a result of the movement that was detected with interferometric technique. On the other hand, the dominant fringes pattern also appears near the coastal area, this is consistent with the field investigations two days after the earthquake. However, the study has also limitations of resolution and atmospheric artefacts in SAR interferograms. The atmospheric artefacts are caused by changes in the atmospheric refractive index of the medium, as a result, has limitation to produce coherence image. Low coherence will be affected the result in creating fringes (movement can be detected by fringes). The spatial resolution of the Sentinel satellite has not been sufficient for studying land surface deformation in this area. Further studies will also be investigated using both ALOS and TerraSAR-X. ALOS and TerraSAR-X improved the spatial resolution of SAR satellite.

Keywords: earthquake, InSAR, interferometric, Sentinel-1A

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Authors: Anusha Konduru, Zhiyong Shan, Preethi Santhanam, Vinod Namboodiri, Rajiv Bagai

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Mobile applications are increasing in a significant amount, each to address the requirements of many users. However, the quick developments and enhancements are resulting in many underlying defects. Android apps create and handle a large variety of 'instance' data that has to persist across runs, such as the current navigation route, workout results, antivirus settings, or game state. Due to the nature of Android, an app can be paused, sent into the background, or killed at any time. If the instance data is not saved and restored between runs, in addition to data loss, partially-saved or corrupted data can crash the app upon resume or restart. However, it is difficult for the programmer to manually test this issue for all the activities. This results in the issue of data loss that the data entered by the user are not saved when there is any interruption. This issue can degrade user experience because the user needs to reenter the information each time there is an interruption. Automated testing to detect such data loss is important to improve the user experience. This research proposes a tool, DroidDL, a data loss detector for Android, which detects the instance data loss from a given android application. We have tested 395 applications and found 12 applications with the issue of data loss. This approach is proved highly accurate and reliable to find the apps with this defect, which can be used by android developers to avoid such errors.

Keywords: Android, automated testing, activity, data loss

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Authors: James Moir

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The mainstay of Big Data is prediction in that it allows practitioners, researchers, and policy analysts to predict trends based upon the analysis of large and varied sources of data. These can range from changing social and political opinions, patterns in crimes, and consumer behaviour. Big Data has therefore shifted the criterion of success in science from causal explanations to predictive modelling and simulation. The 19th-century science sought to capture phenomena and seek to show the appearance of it through causal mechanisms while 20th-century science attempted to save the appearance and relinquish causal explanations. Now 21st-century science in the form of Big Data is concerned with the prediction of appearances and nothing more. However, this pulls social science back in the direction of a more rule- or law-governed reality model of science and away from a consideration of the internal nature of rules in relation to various practices. In effect Big Data offers us no more than a world of surface appearance and in doing so it makes disappear any context-specific conceptual sensitivity.

Keywords: big data, appearance, disappearance, surface, epistemology

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Authors: Stepan Papacek, Jiri Jablonsky, Radek Kana, Ctirad Matonoha, Stefan Kindermann

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FRAP (Fluorescence Recovery After Photobleaching) is a widely used measurement technique to determine the mobility of fluorescent molecules within living cells. While the experimental setup and protocol for FRAP experiments are usually fixed, data processing part is still under development. In this paper, we formulate and solve the problem of data selection which enhances the processing of FRAP images. We introduce the concept of the irrelevant data set, i.e., the data which are almost not reducing the confidence interval of the estimated parameters and thus could be neglected. Based on sensitivity analysis, we both solve the problem of the optimal data space selection and we find specific conditions for optimizing an important experimental design factor, e.g., the radius of bleach spot. Finally, a theorem announcing less precision of the integrated data approach compared to the full data case is proven; i.e., we claim that the data set represented by the FRAP recovery curve lead to a larger confidence interval compared to the spatio-temporal (full) data.

Keywords: FRAP, inverse problem, parameter identification, sensitivity analysis, optimal experimental design

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Authors: Nabilah Filzah Mohd Radzuan, Zalinda Othman, Azuraliza Abu Bakar, Abdul Razak Hamdan

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Uncertain data is believed to be an important issue in building up a prediction model. The main objective in the time series uncertainty analysis is to formulate uncertain data in order to gain knowledge and fit low dimensional model prior to a prediction task. This paper discusses the performance of a number of techniques in dealing with uncertain data specifically those which solve uncertain data condition by minimizing the loss of compression properties.

Keywords: compression properties, uncertainty, uncertain time series, mining technique, weather prediction

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Authors: William Middleton, Nodumo Zulu, Sue Harrison

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Open raceway ponds are currently the most used system for the commercial cultivation of algal biomass, as it is a cost-effective means of production. However, raceway ponds suffer from lower algal productivity when compared to closed photobioreactors. This is due to poor gas exchange between the fluid and the atmosphere. Carbon dioxide (CO₂) mass transfer is a large concern in the production of algae in raceway pond systems. The utilization of atmospheric CO₂ does not support maximal growth; however, CO₂ supplementation in the form of flue gas or concentrated CO₂ is not cost-effective. The introduction of slopes into the raceway system presents a possible improvement to the mass transfer from the air, as seen in previous work conducted at CeBER. Slopes improve turbulence (decreasing the concentration gradient of dissolved CO₂) and can cause air entrainment (allowing for greater surface area and contact time between the air and water). This project tests the findings of previous studies conducted in an indoor lab-scale raceway on a larger scale under outdoor conditions. The addition of slopes resulted in slightly increased CO₂ mass transfer as well as algal growth rate and productivity. However, there were reductions in energy consumption and average fluid velocity in the system. These results indicate a potential to improve the economic feasibility of algal biomass production, but further economic assessment would need to be carried out.

Keywords: algae, raceway ponds, mass transfer, algal culture, biotechnology, reactor design

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Authors: Maram Saleh

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Knowledge has become an essential resource in today’s economy and become the most important asset of maintaining competition advantage in organizations. The importance of knowledge has made organizations to manage their knowledge assets and resources through all multiple knowledge management stages such as: Knowledge Creation, knowledge storage, knowledge sharing and knowledge use. Researches on data mining are continues growing over recent years on both business and educational fields. Data mining is one of the most important steps of the knowledge discovery in databases process aiming to extract implicit, unknown but useful knowledge and it is considered as significant subfield in knowledge management. Data miming have the great potential to help organizations to focus on extracting the most important information on their data warehouses. Data mining tools and techniques can predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. This review paper explores the applications of data mining techniques in supporting knowledge management process as an effective knowledge discovery technique. In this paper, we identify the relationship between data mining and knowledge management, and then focus on introducing some application of date mining techniques in knowledge management for some real life domains.

Keywords: Data Mining, Knowledge management, Knowledge discovery, Knowledge creation.

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Authors: Victor Onomza Waziri, John K. Alhassan, Idris Ismaila, Noel Dogonyara

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This paper describes the problem of building secure computational services for encrypted information in the Cloud. Computing without decrypting the encrypted data; therefore, it meets the yearning of computational encryption algorithmic aspiration model that could enhance the security of big data for privacy or confidentiality, availability and integrity of the data and user’s security. The cryptographic model applied for the computational process of the encrypted data is the Fully Homomorphic Encryption Scheme. We contribute a theoretical presentations in a high-level computational processes that are based on number theory that is derivable from abstract algebra which can easily be integrated and leveraged in the Cloud computing interface with detail theoretic mathematical concepts to the fully homomorphic encryption models. This contribution enhances the full implementation of big data analytics based on cryptographic security algorithm.

Keywords: big data analytics, security, privacy, bootstrapping, Fully Homomorphic Encryption Scheme

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Authors: Yanisa Chaiya, Jintana Sanwong

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Let V be a vector space over a field and W a subspace of V. Let Fix(V,W) denote the set of all linear transformations on V with fix all elements in W. In this paper, we show that Fix(V,W) is a semigroup under the composition of maps and describe Green’s relations on this semigroup in terms of images, kernels and the dimensions of subspaces of the quotient space V/W where V/W = {v+W : v is an element in V} with v+W = {v+w : w is an element in W}. Let dim(U) denote the dimension of a vector space U and Vα = {vα : v is an element in V} where vα is an image of v under a linear transformation α. For any cardinal number a let a'= min{b : b > a}. We also show that the ideals of Fix(V,W) are precisely the sets. Fix(r) ={α ∊ Fix(V,W) : dim(Vα/W) < r} where 1 ≤ r ≤ a' and a = dim(V/W). Moreover, we prove that if V is a finite-dimensional vector space, then every ideal of Fix(V,W) is principle.

Keywords: Green’s relations, ideals, linear transformation semi-groups, principle ideals

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Authors: Pitsanu Poonpetpun, Witthaya Mekhum, Warangkana Kongsil

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This research was an attempt to study work motivation of employees in Siam Thai Co., Ltd. The study took place in Rayong with 59 employees as participants. The research tool was questionnaires which consisted of sets of questions about company’s policy, management, executives and good relationship within the firm. The questionnaires style was rating scale with 5 score bands. The questionnaires were analyzed by percentage, frequency, mean and standard deviation. From the study, the result showed that policy and management were in moderate scale, executive and managers were in moderate scale and relationship within the firm were in high scale.

Keywords: motivation, job, performance, employees

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Authors: Murat Yildiz, Burcu Yildiz

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Price Cap Regulation is a form of economic regulation designed in the 1980s in the United Kingdom. Price cap regulation sets a cap on the price that the utility provider can charge. The cap is set according to several economic factors, such as the price cap index, expected efficiency savings and inflation. It has been used by several countries as a regulatory regime in several sectors. Container port privatization is still in early stages in some countries. Lack of a general framework can be an impediment to privatization. This paper aims a general framework to comprising decisions to be made for variables which are able to accommodate the variety of container terminals. Several approaches that may be needed as well as a passage between approaches.

Keywords: Price Cap Regulation, ports privatization, container terminal price regime, earning sharing

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Authors: Sarisa Pinkham, Kanyarat Bussaban

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The research aims to approximate the amount of daily rainfall by using a pixel value data approach. The daily rainfall maps from the Thailand Meteorological Department in period of time from January to December 2013 were the data used in this study. The results showed that this approach can approximate the amount of daily rainfall with RMSE=3.343.

Keywords: daily rainfall, image processing, approximation, pixel value data

Procedia PDF Downloads 377

Authors: Avi Shrivastava

Abstract:

Our world continues to grapple with a lot of social issues. Economic growth and scientific advancements have not completely eradicated poverty, homelessness, discrimination and bias, gender inequality, health issues, mental illness, addiction, and other social issues. So, how do we improve the human condition in a world driven by advanced technology? The answer is simple: we will have to leverage technology to address some of the most important social challenges of the day. AI, or artificial intelligence, has emerged as a critical tool in the battle against issues that deprive marginalized and disadvantaged groups of the right to enjoy benefits that a society offers. Social work professionals can transform their lives by harnessing it. The lack of reliable data is one of the reasons why a lot of social work projects fail. Social work professionals continue to rely on expensive and time-consuming primary data collection methods, such as observation, surveys, questionnaires, and interviews, instead of tapping into AI-based technology to generate useful, real-time data and necessary insights. By leveraging AI’s data-mining ability, we can gain a deeper understanding of how to solve complex social problems and change lives of people. We can do the right work for the right people and at the right time. For example, AI can enable social work professionals to focus their humanitarian efforts on some of the world’s poorest regions, where there is extreme poverty. An interdisciplinary team of Stanford scientists, Marshall Burke, Stefano Ermon, David Lobell, Michael Xie, and Neal Jean, used AI to spot global poverty zones – identifying such zones is a key step in the fight against poverty. The scientists combined daytime and nighttime satellite imagery with machine learning algorithms to predict poverty in Nigeria, Uganda, Tanzania, Rwanda, and Malawi. In an article published by Stanford News, Stanford researchers use dark of night and machine learning, Ermon explained that they provided the machine-learning system, an application of AI, with the high-resolution satellite images and asked it to predict poverty in the African region. “The system essentially learned how to solve the problem by comparing those two sets of images [daytime and nighttime].” This is one example of how AI can be used by social work professionals to reach regions that need their aid the most. It can also help identify sources of inequality and conflict, which could reduce inequalities, according to Nature’s study, titled The role of artificial intelligence in achieving the Sustainable Development Goals, published in 2020. The report also notes that AI can help achieve 79 percent of the United Nation’s (UN) Sustainable Development Goals (SDG). AI is impacting our everyday lives in multiple amazing ways, yet some people do not know much about it. If someone is not familiar with this technology, they may be reluctant to use it to solve social issues. So, before we talk more about the use of AI to accomplish social work objectives, let’s put the spotlight on how AI and social work can complement each other.

Keywords: social work, artificial intelligence, AI based social work, machine learning, technology

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Authors: Chung-Yuan Huang, Yu-Hsiang Fu, Chuen-Tsai Sun

Abstract:

Community detection represents an important data-mining tool for analyzing and understanding real-world complex network structures and functions. We believe that at least four criteria determine the appropriateness of a community detection algorithm: (a) it produces useable normalized mutual information (NMI) and modularity results for social networks, (b) it overcomes resolution limitation problems associated with synthetic networks, (c) it produces good NMI results and performance efficiency for Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks, and (d) it produces good modularity and performance efficiency for large-scale real-world complex networks. To our knowledge, no existing community detection algorithm meets all four criteria. In this paper, we describe a simple hierarchical arc-merging (HAM) algorithm that uses network topologies and rule-based arc-merging strategies to identify community structures that satisfy the criteria. We used five well-studied social network datasets and eight sets of LFR benchmark networks to validate the ground-truth community correctness of HAM, eight large-scale real-world complex networks to measure its performance efficiency, and two synthetic networks to determine its susceptibility to resolution limitation problems. Our results indicate that the proposed HAM algorithm is capable of providing satisfactory performance efficiency and that HAM-identified communities were close to ground-truth communities in social and LFR benchmark networks while overcoming resolution limitation problems.

Keywords: complex network, social network, community detection, network hierarchy

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Authors: Mohammad Javad Mollakazemi, Farhad Asadi, Aref Ghafouri

Abstract:

In this paper, we considered and applied parametric modeling for some experimental data of dynamical system. In this study, we investigated the different distribution of output measurement from some dynamical systems. Also, with variance processing in experimental data we obtained the region of nonlinearity in experimental data and then identification of output section is applied in different situation and data distribution. Finally, the effect of the spanning the measurement such as variance to identification and limitation of this approach is explained.

Keywords: Gaussian process, nonlinearity distribution, particle filter, system identification

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Authors: Jaya Mathew

Abstract:

Many organizations are faced with the challenge of how to analyze and build Machine Learning models using their sensitive telemetry data. In this paper, we discuss how users can leverage the power of R without having to move their big data around as well as a cloud based solution for organizations willing to host their data in the cloud. By using ScaleR technology to benefit from parallelization and remote computing or R Services on premise or in the cloud, users can leverage the power of R at scale without having to move their data around.

Keywords: predictive maintenance, machine learning, big data, cloud based, on premise solution, R

Procedia PDF Downloads 361

Authors: Magda Kosmal, Anna A. Kuśnierz, Joanna Rybicka-Łada

Abstract:

In the case of waste glass cullet, a fully functioning recycling system for individual glass industries was developed, while recycling of cement dust encounters a number of difficulties and is conducted to a limited extent in the packaging and flat glass industry. The aim of the project was to examine the possibility of using dust arising in cement plants in the process of melting various types of glasses. Dust management has a positive effect on the aspect of environmental protection and ecology. Sets have been designed, and the parameters of the melting process have been optimized. Glasses were obtained with the addition of selected cement dust on a laboratory scale, using DTA, XRD, SEM tests, and a gradient furnace was conducted to check the tendency to crystallization.

Keywords: cement dust, crystallization, glass, XRD, SEM

Procedia PDF Downloads 67